Deep Convolution Neural Networks (Deep CNNs) are the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problem of character recognition, but their use has become as widespread as it is now thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolution neural network became a very useful tool in the field of the machine learning.
A post-processing is a job which applies a predetermined adjustment to a result outputted from a deep CNN in order to obtain a user-desired result additionally.
Recently, the post-processing is frequently used in the deep CNNs. The CNN plays several roles in an autonomous driving module. One of such roles is to detect one or more lanes in an input image. By detecting the lanes, a free space for vehicles to drive through may be detected, or vehicles may be appropriately controlled to drive on the center of a road.
However, if the result only from the deep CNNs is used, the performance of lane detection is not very useful. Hence, the lane detection is often achieved by post-processing the result from the deep CNNs, but, once again, if a segmentation score map alone generated in the deep CNNs is used, the performance of the post-processing is poor.